Gareth Anderson - Distressed Banks, Distorted Decisions

Distressed Banks, Distorted Decisions

Gareth Anderson, University of OxfordRebecca Riley, NIESRGarry Young, NIESR

January 2018

Motivation

Figure: 5 year CDS premia

Four banking groups account for around 80% of loans to UK firms.The crisis had a heterogeneous impact on these banking groups.

Contribution

Exploit an exogenous source of credit constraints faced by UK firms,induced by banking relationships they maintained prior to crisis.Use natural experiment approach to assess whether the exit margin offirms was distorted by credit constraints following the crisis.

Main Messages

Maintaining relationships with Distressed Banks increased theprobability of firms exiting.Increased probability of exiting was not concentrated in the mostunproductive firms.

I Tight credit may have limited the cleansing impact of the recession.I While micro impact is significant, macro impact is limited.

Related Literature: Credit Constraints and Firm Activity

Global financial crisis provides a natural experiment for studying theimpact of tight credit conditions on firm activity.Offers a solution to disentangling credit demand and credit supply.Tighter credit conditions following the crisis adversely affected firminvestment and firm employment (e.g. Duchin et al. (2010) andBentolila et al. (2013)).

Related Literature: Credit Constraints and Firm Dynamics

Contrasting views on how recessions and crises affect the process offirm entry and firm exit.“Cleansing” view: recession accelerates the exit of inefficient firmswhich are insulated during boom times. (e.g. Schumpeter (1934);Caballero and Hammour (1994)).“Sullying” / “Scarring” view: frictions associated with recessions mayadversely affect productivity (Barlevy (2002, 2003); Ouyang (2009);Osotimehin and Pappadà (2016)).

Productivity Distortions from Banking Crises

Firms that need external credit to survive may be more productive(e.g. Barlevy (2003)).Less productive firms which are less reliant on bank finance facereduced competition.Forbearance/ ”zombie lending” (e.g. Peek and Rosengren (2005);Arrowsmith et al. (2013); Roland (2016)).

Model: Credit frictions and productivity cutoffs

Figure: Impact of credit market frictions on productivity cutoffs

Model: Productivity distribution

0 0.5 1 1.5 2 2.5 3 3.5 40

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Figure: Impact of credit market frictions on the productivity distribution

Related Literature: Credit Constraints and Firm Dynamics

Recent empirical studies have found some support for the view thatcredit constraints can weaken the “cleansing” effect of recessions.Evidence that the negative relationship between firm productivity andthe probability of exit weakened following the Great Recession (e.g.Foster et al. (2016); Harris and Moffat (2016)).Some highly productive, credit constrained firms are forced to exit(e.g. Eslava et al. (2010); Hallward-Driemeier and Rijkers (2013)).

Distressed and Non Distressed Banks

Follow the approach of Bentolila et al. (2013):I Define Distressed Banks as those which obtained state funding between

2008 and 2009 or required a takeover.I Define NonDistressed Banks as those which did not receive state

funding and did not require a takeover.I Divide firms into Treatment and Control groups based on which banks

they had relationships with in 2008.

Identification: Distressed and Non Distressed Banks

For identification, require thatI i) Credit supply conditions tightened by more for firms which had

pre-crisis relationships with Distressed Banks.I ii) Firms subject to a tightening of credit conditions cannot easily

switch bank.


i) Credit supply conditions tightened by more for firms which hadpre-crisis relationships with Distressed Banks.

I CDS premiums of banks similar prior to crisis, but different followingcrisis. Increase in CDS spreads particularly pronounced for RBS andLBG.

I Little evidence that lending commitments made by Distressed Banks inreturn for public support influenced their lending behaviour.

I Overall, evidence suggests the contraction in credit supply byDistressed Banks was greater than that of NonDistressed Banks.


ii) Firms cannot easily switch bank.I Asymmetric information makes switching difficult.I Bank switching by UK firms is low, with no pick-up following crisis.

Figure: Evolution of Switching Rates Over Time

Data: Corporate Balance Sheet

Consider key measures of corporate activity (2002-2012 BvD/FAME).Control for firm age, relationship length, foreign owned firms,exporters, credit score band, court judgements, firm size, account type.

Data: Corporate Banking Relationships

Lenders commonly require companies to provide security against aloan, in the form of a mortgage, a floating charge or a fixed charge.In the UK, registered companies are required to report charges andmortgages to Companies House within 21 days of their creation date.We identify firm-bank relationships using a textual algorithm.

Empirical Specification

Baseline specification- Linear probability model.How does having a relationship with a Distressed Bank at time t affectthe likelihood of exit in the subsequent period for firm i in industry j :

Yi ,t = gj ,+Xi ,tk +b1⇥Distressed Banki ,t +b2⇥Post Crisist (1)+b3⇥Distressed Banki ,t ⇥Post Crisist + ei ,t

where

Yi ,t is an indicator variable equal to 1 if firm i subsequentlyexits in the specified time frame and 0 otherwise

Summary Statistics, by Banking Relationship

2004 2008

ND D No Bank ND D No Bank

Exit in 2 years 11% 10% 13% 11% 11% 14%

Exit in 4 years 21% 20% 26% 19% 20% 25%

Start-Up 14% 13% 33% 6% 8% 24%

Young 34% 33% 58% 26% 28% 56%

Foreign Owned 2% 3% 3% 2% 3% 2%

Exporter 1% 2% 1% 2% 1% 1%

Observations 66334 70695 403407 70400 81237 546880

Results: Baseline specification

Table: Baseline model

2 Year Exit 3 Year Exit 4 Year Exit

Distressed -0.001 0.001 0.001(0.001) (0.002) (0.002)

Post-Crisis -0.001 0.012⇤⇤⇤ 0.026⇤⇤⇤

(0.002) (0.005) (0.006)Distressed * Post-Crisis 0.006⇤⇤⇤ 0.006⇤⇤⇤ 0.008⇤⇤⇤

(0.001) (0.002) (0.002)

Industry Fixed Effects Yes Yes YesFirm Controls Yes Yes YesR-Squared 0.143 0.112 0.117Observations 303953 300244 288648

Robust standard errors. ***, **, * shows significance at the 1%, 5% and 10% levels.

Results: Baseline specification

Change in probability of exit significantly higher for firms withDistressed Banks following the crisis than firms attached toNonDistressed Banks.Change in the probability of exit within 2 years was around 0.6percentage points higher for firms which had a relationship withDistressed Banks relative to firms with NonDistressed Banks.

Exit Dynamics and Productivity

Consider a smaller sample of firms for which we are able to calculate aproxy for their gross value added productivity:

Productivityi ,t =GVAi ,t

Employees

where

GVAi ,t is a proxy of gross value added in real terms given bythe sum of a firm’s reported Operating Profits and theCost Of Employees, deflated by industry deflators.


Split the observations into productivity quintiles, based on proxy forgross value added productivity.

Figure: Firms which exited andbanked with “Non-Distressed” banks

Figure: Firms which exited andbanked with “Distressed” banks


Interact variables of interest in our Baseline specification(Distressed Bank , Post Crisis and Post Crisis⇥Distressed Bank) withindicator variables for the productivity quintile a firm is in:

Yi ,j ,t, = gj +Xi ,tk +5

Âk=1

b1,k(Distressed Banki ⇥Prodi ,k,t) (2)

+5

Âk=1

b2,k(Post Crisist ⇥Prodi ,k,t)

+5

Âk=1

b3,k(Distressed Banki ⇥Post Crisist ⇥Prodi ,k,t)+ ei ,j ,t

Table: Firm Exit, by Productivity Quintile


Lowest Productivity Quintile

Distressed * Post-Crisis -0.036⇤ -0.033⇤ -0.020

(0.018) (0.018) (0.032)

Productivity Quintile 2

Distressed * Post-Crisis 0.011 0.022 0.022

(0.016) (0.020) (0.021)


Distressed * Post-Crisis 0.031⇤⇤⇤ 0.017 0.025

(0.011) (0.016) (0.017)


Distressed * Post-Crisis 0.012 0.003 0.018

(0.010) (0.012) (0.016)

Highest Productivity Quintile

Distressed * Post-Crisis 0.019⇤ 0.011 0.014

(0.010) (0.010) (0.014)

Industry Fixed Effects Yes Yes Yes

Firm Controls Yes Yes Yes

R-squared 0.227 0.168 0.204

Observations 18284 18638 19016

Robust standard errors. ***, **, * shows significance at the 1%,5% and 10% significance levels.

Table: Firm Exit, by Leverage and Productivity

Lowest Leverage Tercile Middle Leverage Tercile Highest Leverage Tercile

2 Year Exit 4 Year Exit 2 Year Exit 4 Year Exit 2 Year Exit 4 Year Exit


Distressed * Post-Crisis 0.008 -0.003 -0.032 0.015 -0.081⇤⇤ -0.049

(0.029) (0.041) (0.034) (0.047) (0.034) (0.058)


Distressed * Post-Crisis 0.026 0.024 -0.014 0.012 0.019 0.018

(0.024) (0.025) (0.023) (0.031) (0.040) (0.040)


Distressed * Post-Crisis 0.005 -0.007 0.020 0.044⇤ 0.069⇤⇤ 0.010

(0.016) (0.033) (0.017) (0.025) (0.030) (0.035)


Distressed * Post-Crisis 0.008 -0.008 0.026⇤ 0.030 0.006 0.012

(0.016) (0.017) (0.015) (0.025) (0.023) (0.036)


Distressed * Post-Crisis 0.011 -0.010 -0.006 0.005 0.054 0.044

(0.015) (0.022) (0.022) (0.024) (0.035) (0.041)

Industry Fixed Effects Yes Yes Yes Yes Yes Yes

Firm Controls Yes Yes Yes Yes Yes Yes

R-Squared 0.191 0.162 0.145 0.145 0.295 0.278

Observations 6032 6274 6034 6275 6218 6467

Robust standard errors. ***, **, * shows significance at the 1%,5% and 10% significance levels.

Robustness: Placebo Crises

DiD specification relies on the assumption of parallel pre-crisis trends.Undertake placebo tests considering alternative placebo “crises”.Consider the impact of having relationships with Distressed Banks onthe two year exit probability for two placebo “crises”: 2004 and 2006.

Robustness: Placebo Crises

Table: Placebo Crises, 2 Year Exit Rate

Placebo “Crisis”=2004 Placebo “Crisis”=2006 True Crisis=20082 year exit 2 year exit 2 year exit

Distressed * “Crisis” -0.003 -0.003 0.006⇤⇤⇤

(0.003) (0.002) (0.001)



Robustness: Weighted Regression

Productivity sample limited to firms which report Operating Profits,Employees and Cost Of Employees.Under-represents smaller firms.Weight observations in productivity sample to match the number offirms in each industry-size-bank group-year cell in baseline sample.

Table: Firm Exit, Weighted Regression

2 year exit 3 year exit 4 year exit


Distressed * 2008 -0.003 0.028 0.024

(0.031) (0.043) (0.054)


Distressed * 2008 0.016 -0.020 0.038

(0.041) (0.056) (0.042)


Distressed * 2008 0.058⇤⇤ 0.031 0.001

(0.027) (0.031) (0.044)


Distressed * 2008 0.033 -0.025 0.026

(0.022) (0.044) (0.050)


Distressed * 2008 0.039 0.056⇤ 0.035

(0.028) (0.031) (0.030)

Industry Fixed Effects Yes Yes Yes

Firm Controls Yes Yes Yes

R-Squared 0.276 0.252 0.298

Observations 18284 18638 19016


Robustness: Alternative Measure of Productivity

Productivity sample limited to firms which report Operating Profits,Employees and Cost Of Employees.Use an alternative measure of productivity, given by the ratio of GVAto the cost of employees, following Giordano et al. (2015).Productivity_ai ,t =

GVAi ,t

Cost Of Employees

Table: Firm Exit, Alternative Measure of Productivity


Lowest Productivity QuintileDistressed * 2008 -0.027⇤ -0.056⇤⇤ -0.018

(0.015) (0.024) (0.021)Productivity Quintile 2Distressed * 2008 0.036⇤⇤ 0.028 0.032

(0.017) (0.025) (0.020)Productivity Quintile 3Distressed * 2008 -0.003 0.012 0.018

(0.009) (0.013) (0.015)Productivity Quintile 4Distressed * 2008 0.007 0.017 0.025

(0.010) (0.014) (0.020)Highest Productivity QuintileDistressed * 2008 0.017 0.004 0.006

(0.012) (0.017) (0.016)



Conclusion

Exploit pre-crisis banking relationships as an exogenous source ofcredit constraints faced by UK firms.Credit constraints faced by UK firms following the financial crisis hada detrimental impact on their probability of survival.Credit constraints distorted productivity distribution of exiters.Some credit constrained firms may have been forced to exit theirindustry, despite being more productive than surviving competitors.While micro impact is significant, macro impact is limited.

Model: Overview

Closed economy Melitz (2003) model with credit constraints, a’laManova (2013).Firms must finance a fraction of their fixed costs upfront, usingfinancial intermediaries.Fraction of fixed cost to be financed varies across firms.Credit market frictions raise the productivity threshold for firms.Firms more dependent on external finance are more adversely affectedby credit market frictions.

Model: Consumers

Constant elasticity of substitution (CES) preferences over a continuumof goods indexed by w over ⌦:

U =⇥R

w2⌦ q(w)rdw⇤ 1

r

where s = 11�r > 1

With optimal consumption and expenditure for different varieties givenby:

q(w) = Qhp(w)P

i�s

r(w) = p(w)q(w) = Rhp(w)P

i1�s

Model: Producers

Continuum of firms, each of which produces a variety w .Entrants are required to pay an entry cost, fe , draw productivity level,j , from g(j)Labour consists of a fixed cost f and a variable cost which depends ona firm’s productivity, j :

l = qj + f

Model: Producers

Firms have to pay a fraction, di of fixed cost, f , upfront.Fraction low, dL , with probability c or high, dH , with probability1�c .Upfront fixed cost financed by a financial intermediary.Financial contract: at start of period, firms offer repayment F to bepaid at the end of the periodIntermediaries obtain agreed repayment F with probability l 1.With probability 1�l , firm defaults and the intermediary does notreceive F , but seizes collateral tfe . from the firm.In the case of default, the firm replaces its collateral, tfe .

Model: Producers

Upon entry, the problem of the firm is to choose its price, quantityand repayment to maximise profits subject to three constraints:

max

p(j),q(j),F (j,di )p(j)q(j)�

hq(j)

j +(1�di )f +lF (j,di )+(1�l )tfei

subject to

(1) q(j) = Qhp(j)P

i�s

(2) F (j,di ) p(j)q(j)� q(j)j � (1�di )f

(3) di f lF (j,di )+(1�l )tfe

Assume constraint (3) binds with equality.Define a productivity threshold, j⇤

di, below which producers choose

not to produce.

Model: Comparative Statics

Solve the model to find the two productivity thresholds, j⇤dL

and j⇤dH

.To illustrate comparative statics, calibrate the model, closely followingMelitz and Redding (2013).Consider how contract imperfections, given by l , affect the cutoffproductivities and the productivity distribution.

Model: Credit frictions and productivity cutoffs

Figure: Impact of credit market frictions on productivity cutoffs

Model: Productivity distribution

0 0.5 1 1.5 2 2.5 3 3.5 40

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Figure: Impact of credit market frictions on the productivity distribution

Date post:	22-Jan-2018
Category:	Economy & Finance
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Gareth Anderson - Distressed Banks, Distorted Decisions

Economy & Finance